Bibliographic record
Abstract
Shea tree seedling root noodles also known as black dots were detected three months after the application of Arbuscular Mycorrhizal Fungus (AMF) Acaulospora scrobiculatu in roots of Shea tree seedlings.The present study was carried out to detect the presence of mycorrhiza on the roots of Shea tree seedlings with the view to know whether it support the growth of the seedlings in the nursery The 300 gram application of AMF lead to the highest colonization rate observed with 59.00%.The 100 gram application also had colonization rate with 42.14% when compared with non application of AMF at 0.0 gram which recorded 0.00% colonization rate while the second non application of AMF also at 0.0 gram recorded 8.90% colonization rate.This suggests there were significant differences between treatment L with 300 g at 59.00% and treatment k with 100 g at 42.14% compared with treatment G with 0.0 g at 8.10%, A with 0.0 g at 0.00%, and D also with 0.0 g at 0.00% respectively.The application of AMF best supported the growth of Shea tree seedlings in the nursery with more leaves, thickness of the roots, black dots and influencing the growth of mycorrhizal hyphae.Non application of AMF was less suitable in supporting growth.In the absence of AMF, rich top soil of decomposed organic matters should be used.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.950 | 0.940 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".